Distribution fault location and grid analytics at hydro Ottawa
Bibliographic record
Abstract
In 2014, Hydro Ottawa began a pilot project to use power quality monitors to locate faults on distribution feeders supplied by medium voltage (MV) systems. Fault measurements captured by the meters are downloaded automatically, integrated into a relational database, and processed for reactance calculations. The reactance calculations are combined with detailed distribution circuit models and geographic information system data to build estimated fault location tables and web-based map displays. The systems are integrated on the company intranet and used in real-time by numerous groups within Hydro Ottawa including power quality engineers, control room operators, field operations, and distribution planning. The algorithm used for waveform processing can distinguish between single-phase faults, multi-phase faults, subcycle faults, and feeder energizing magnetizing inrush. This paper will present an overview of some of the parameters and practices for finding faults at Hydro Ottawa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".